Cross well electromagnetic tomography (“EM”) has been available as a reservoir evaluation technology for approximately fifteen years. To obtain cross well electromagnetic tomography data, one needs to locate a receiver in one well and a transmitter in another well. The receiver would stay at one depth while the transmitter travels up and down the well while transmitting electromagnetic signals. The receiver receives the signal and transmits the signal up to the surface, where the received signal is recorded against the depth of both the transmitter and the receiver. The receiver would then be moved to another depth and the transmitter again travels up and down the well transmitting signals to be received by the receiver. At the end of an EM log, the recorder would have recorded a complete set of measurement response corresponding to the receiver and the transmitter each at different depths.
The recorded raw measurement response, however, are not useful on their own. To make use of the measurement response, one must recreate a reservoir model with characteristics that make sense to the engineer. According to the prior art, one needs to use the inversion method to obtain data of any practical use. To do so one needs to create a cellular model of resistivity distribution, commonly based on borehole resistivity measurements. A tool response forward model is then applied to this cellular model to predict the measurement apparatus response. The predicted measurement apparatus response is then compared to the actual measurement response obtained from the EM log, and the cellular model is then modified. The modification is made on the resistivity values at each of the blocks of the cellular model. In order to make an appropriate modification to the reservoir property values, the optimization formulation of the cellular model must be capable of calculating modification values to be added to or subtracted from each reservoir property value for each block of the cellular model. A tool response forward model is again applied to this cellular model to predict the measurement apparatus response. This process is repeated iteratively until the simulation “converges”, or in other words, some cost function representing the actual measurement apparatus response and the predicted measurement apparatus response is optimized.
This known approach has at least three significant drawbacks. First of all, the inversion method is very computational intensive and time consuming. The forward calculation as well as the calculation of the modification values usually consume a large amount of the computer CPU power. Furthermore, multiple iterations are required to reach convergence or to exhaust the predetermined maximum number of iteration. Usually the larger the reservoir model, or the larger the difference between the reservoir property values of adjacent blocks, the longer it takes to obtain convergence.
Secondly, results of the inversion method could be misleading. The inversion method generally provides a single answer, which the inexperienced end user may consider a unique answer. In fact, most geophysical inversion processes are massively underdetermined. There are usually more than one “convergence points” in each mathematical reservoir model, and convergence at any of the convergence points may not necessarily reflect the truth of the reservoir properties. Thus the answer derived from the inversion process may be the answer that best matches the optimization mathematical criteria, but it does not necessarily reflect the physically correct answer. In addition, generally inversion schemes do not provide any information about the range of possible answers that may all be supported almost equally well by the measured data.
Thirdly, and most importantly, data obtained through the inversion method may not be useful for the reservoir engineer. The inversion method ultimately provides a two- or three-dimensional subsurface resistivity model or image. However, such a resistivity model is not useful in and of itself. Resistivity is not a property that a reservoir engineer can use to predict the reservoir performance in any meaningful way. A reservoir engineer normally uses data such as porosity, permeability, saturation, salinity, etc, which he can use to predict reservoir production performance or implement reservoir production plans. Resistivity is a function of reservoir properties such as porosity, saturation, and salinity. Thus a resistivity value corresponds to a large number of permutations of values of porosity, saturation, and salinity. Therefore resistivity is not a reservoir property that a reservoir engineer can use directly to predict reservoir performance or plan production management. The resistivity model obtained through the inversion method requires further interpretation to place it in a geological or reservoir engineering context. What makes the problem even worse is that resistivity is a function of several directly useful reservoir properties including porosity, water saturation, water salinity, etc. A reservoir engineer does not have any tool to guide him to obtain these useful reservoir properties from a resistivity value. The inversion scheme or inversion results do not provide any guidance on the important step of obtaining directly useful reservoir properties.